
Matrix Completion Sub-Challenge: given remote sensing images taken at different locations and dates, and in different modalities, participants are required to predict appearance at a novel [lon, lat, date, modality] query. Performance is measured on the following visual metrics: Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM) [1]}, Learned Perceptual Image Patch Similarity (LPIPS) [2]}, and Fréchet Inception Distance (FID) [3]}. Detailed challenge description is provided in Section REF .

Deforestation Estimation Sub-Challenge: beyond visual appearance, participants are required to classify whether a region is deforested or not at a novel
[lon, lat, date, modality] query. Modality will be `deforestation' for this sub-challenge. Participants will be given the multimodal remote sensing dataset along with the deforestation label maps. Performance is measured on the following metrics: pixel accuracy, F1 score, and Intersection over Union (IoU). Detailed description of this sub-challenge can be found in Section REF .

Image-to-Image Translation Sub-Challenge: participants are required to predict a set of possible cloud-free corresponding optical images given an input SAR image. For this sub-challenge, we provide an aligned dataset (e.g. \([\mathbf {x} ,[\mathbf {y}_1,\mathbf {y}_2,...,\mathbf {y}_N]]\) ) where an input SAR image \(\mathbf {x}\)  is paired to a set of ground truth optical images \([\mathbf {y}_1,\mathbf {y}_2,...,\mathbf {y}_N]\) . Performance is evaluated based on \(\sum _j \min _i \Vert f(\mathbf {x})_i - \mathbf {y}_j \Vert \)  where \(f(\cdot )\)  is a prediction of \(\mathbf {y}\)  given \(\mathbf {x}\) , and \(f\)  may make multiple predictions indexed by \(i\) . Detailed guideline of the sub-challenge is provided in Section REF .

